PROJECT SUMMARY/ ABSTRACT Autism Spectrum Disorder (ASD) is highly heterogeneous in its clinical presentation, which complicates diagnosis, prognosis and treatment. As the genetic basis for ASD becomes increasingly understood, we are challenged to understand how genetic risk translates to specific phenotypes, trajectories and mechanisms. The deep phenotyping and treatment of children who are minimally verbal (MV) provides a unique opportunity to identify potential genetic risk factors or predictors, which is the major aim of this genetics project. Moreover, the addition of the MV ASD cohort to the broader collection of autism families available for molecular study will enhance our understanding of the genetic heterogeneity underlying ASD as a whole. This project will be undertaken by Daniel Geschwind, MD, PhD, and his colleagues at UCLA. We hypothesize that subgroups of MV children may represent genetically more-homogeneous cohorts of ASD with characteristic genetic profiles. Specific syndromes associated with ASD have specific behavioral and cognitive profiles, including language phenotypes (Barnett and van Bon, 2015; D'Angelo et al., 2016; DiStefano et al., 2016; Niklasson et al., 2009; Penagarikano and Geschwind, 2012; van Bon et al., 2010). Further, rare, large effect size de novo mutations are more likely to be associated with more severe phenotypes, including low IQ and severe language impairment (Geschwind and Konopka, 2009; Robinson et al., 2014; Sanders et al., 2015). Genetic testing using microarrays and whole-exome sequencing (WES) is clinically indicated in the evaluation of ASD to identify large effect pathogenic copy number variants (CNV) and de novo protein disrupting mutations (Jeste and Geschwind, 2014; Miller et al., 2010; Schaefer et al., 2008). Given that the prevalence of CNV is associated with severity, e.g., those with low IQ and language impairment may have a frequency of CNV as high as 25% (Jacquemont et al., 2006) and the yield of WES is likely 10-20%, we will use SNP microarrays to identify likely causal CNVs and WES, thereby providing a ?molecular diagnosis? for a subset of patients. We will evaluate the correlation between ASD liability due to common genetic variation genome-wide and quantitative measures of oromotor and auditory functioning (Projects 1 and 2); speech and language ability (Core B); and response to intervention (Project 3). We hypothesize that genetic profile may correlate with response to language intervention (Project 3), with the future goal of taking a first step toward generating predictors that may be useful to prospectively place children into the most appropriate interventions.